Morphable Models for the Analysis and Synthesis of Complex Motion Patterns
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
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Coupled degrees-of-freedom exhibit correspondence, in that their trajectories influence each other. In this paper we add evidence to the hypothesis that spatiotemporal correspondence (STC) of distributed actuators is a component of human-like motion. We demonstrate a method for making robot motion more human-like, by optimizing with respect to a nonlinear STC metric. Quantitative evaluation of STC between coordinated robot motion, human motion capture data, and retargeted human motion capture data projected onto an anthropomorphic robot suggests that coordinating robot motion with respect to the STC metric makes the motion more human-like. A user study based on mimicking shows that STC-optimized motion is (1) more often recognized as a common human motion, (2) more accurately identified as the originally intended motion, and (3) mimicked more accurately than a non-optimized version. We conclude that coordinating robot motion with respect to the STC metric makes the motion more human-like. Finally, we present and discuss data on potential reasons why coordinating motion increases recognition and ability to mimic.